An Efficient Game-Theoretic Planner for Automated Lane Merging with Multi-Modal Behavior Understanding
L. Zhang (TU Delft - Team Sergio Grammatico)
S. Han (Student TU Delft)
S. Grammatico (TU Delft - Team Bart De Schutter, TU Delft - Team Sergio Grammatico)
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Abstract
In this paper, we propose a novel behavior planner that combines game
theory with search-based planning for automated lane merging.
Specifically, inspired by human drivers, we model the interaction
between vehicles as a gap selection process. To overcome the challenge
of multi-modal behavior exhibited by the surrounding vehicles, we
formulate the trajectory selection as a matrix game and compute an
equilibrium. Next, we validate our proposed planner in the high-fidelity
simulator CARLA and demonstrate its effectiveness in handling
interactions in dense traffic scenarios.